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Hands On Ai Trading With Python Quantconnect And Aws

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Hands On Ai Trading With Python Quantconnect And Aws

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Author : Jiri Pik
language : en
Publisher: John Wiley & Sons
Release Date : 2025-01-29

Hands On Ai Trading With Python Quantconnect And Aws written by Jiri Pik and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-29 with Business & Economics categories.

Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab. Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM. Predict market volatility regimes and allocate funds accordingly. Predict daily returns of tech stocks using classifiers. Forecast Forex pairs' future prices using Support Vector Machines and wavelets. Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs. Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications. Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization. Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch. AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.

High Frequency Ai Trading

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Author : Kaito Amatsuki
language : en
Publisher: Independently Published
Release Date : 2025-03-27

High Frequency Ai Trading written by Kaito Amatsuki and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-27 with Business & Economics categories.

Unlock the Power of Artificial Intelligence in Lightning-Fast Trading-Step-by-Step Strategies with Real Python Code! In the fast-paced world of algorithmic trading, opportunities appear and vanish in milliseconds. To conquer these markets, you must leverage the cutting-edge power of Artificial Intelligence. This comprehensive hands-on guide provides everything you need-clearly illustrated strategies, real use-cases, and complete Python code ready for deployment. Inside you'll discover: Statistical Arbitrage using AI: Master the secrets of exploiting rapid market inefficiencies with real-time statistical arbitrage models. AI-driven Market-Making: Deploy neural networks to anticipate order flow, manage risk, and optimize spreads instantly. Reinforcement Learning for Order Execution: Learn step-by-step to utilize Deep RL agents that dynamically adjust execution strategies, minimizing market impact. LSTM Networks for Price Forecasting: Predict short-term price movements accurately with advanced LSTM techniques and integrate them immediately into your trading workflow. Pattern Recognition in Order Books: Identify profitable micro-structure opportunities hidden in huge volumes of tick data. Event-Driven AI Trading: Capitalize on market-moving events almost instantaneously through innovative algorithms that interpret news releases and sentiment shifts automatically. Exploiting Volatile Market Regimes: Detect and profit from regime shifts using AI-driven regime-switching models that help adapt your strategy on-the-fly. Designed explicitly for actionable, practical learning-every chapter provides concise explanations, clear examples, and fully-tested Python scripts. Whether you're an algorithmic trading expert aiming to elevate your systems or a newcomer looking for precise guidance, this book offers powerful strategies aligned with industry best practices to maximize your trading effectiveness. Stop waiting. Start trading smarter-Take your trading profits and strategies to the next level today!

Hands On Algorithmic Trading With Python

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Author : Deepak Kanungo
language : en
Publisher:
Release Date : 2019

Hands On Algorithmic Trading With Python written by Deepak Kanungo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Artificial intelligence in general and specifically machine learning are becoming increasingly important tools for many industries and enterprises. But one business sector in particular has long since adopted and benefitted from these powerful computing paradigms: investment services. In fact, over the past decade, few other industries and sectors have experienced the frenetic pace of automation as that of the investment management industry, the direct result of algorithmic trading and machine learning technologies. Industry experts estimate that today as much as 70% of the daily trading volume in the United States equity markets is executed algorithmically-by computer programs following a set of predefined rules that span the entire trading process, from idea generation to execution and portfolio management. But although all algorithmic trading is executed by computers, the rules for generating trades are either designed by humans or discovered by machine learning algorithms from training data. Not surprisingly, the ability to create these algorithms, particularly using Python, is in high demand. In this video course, designed for those with a basic level of experience and expertise in trading, investing, and writing code in Python, you learn about the process and technological tools for developing algorithmic trading strategies. You'll examine the pros and cons of algorithmic trading as well as the first steps you'll need to take to "level the playing field" for retail equity investors. You'll explore some of the models that you can apply to formulate trading and investment strategies. You'll also learn about the Pandas library to import, analyze, and visualize data from market, fundamental, and alternative, no-cost sources that are available online. You'll even see how to prepare for competitions that can fund your algorithmic trading strategies. (Note that live trading is beyond the scope of the course.) What you'll learn-and how you can apply it By the end of this video course you'll understand: The advantages and disadvantages of algorithmic trading The different types of models used to generate trading and investment strategies The process and tools used for researching, designing, and developing them Pitfalls of backtesting algorithmic strategies Risk-adjusted metrics for evaluating their performance The paramount importance of risk management and position sizing And you'll be able to: Use the Pandas library to import, analyze, and vis...

Machine Learning For Algorithm Trading

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Author : Mark Broker
language : en
Publisher: Independently Published
Release Date : 2020-11-18

Machine Learning For Algorithm Trading written by Mark Broker and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-18 with categories.

Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading thanks to the artificial intelligence? If so, keep reading: this bundle book is for you! Today, thanks to computer programming and PYTHON we can work with sophisticated machines that can study human behavior and identify underlying human behavioral patterns. Scientists can predict effectively what products and services consumers are interested in. You can also create various quantitative and algorithmic trading strategies using Python. It is getting increasingly challenging for traditional businesses to retain their customers without adopting one or more of the cutting-edge technology explained in this book. MACHINE LEARNING FOR ALGORITHM TRADING will introduce you many selected tips and breaking down the basics of coding applied to finance. You will discover as a beginner the world of data science, machine learning and artificial intelligence with step-by-step guides that will guide you during the code-writing learning process. The following list is just a tiny fraction of what you will learn in this bundle PYTHON FOR DATA SCIENCE ✅ Differences among programming languages: Vba, SQL, R, Python ✅ 3 reasons why Python is fundamental for Data Science ✅ Introduction to some Python libraries like NumPy, Pandas, Matplotlib, ✅ 3 step system why Python is fundamental for Data Science ✅Describe the steps required to develop and test an ML-driven trading strategy. PYTHON CRASH COURSE ✅ A Proven Method to Write your First Program in 7 Days ✅ 3 Common Mistakes to Avoid when You Start Coding ✅ Fit Python Data Analysis to your business ✅ 7 Most effective Machine Learning Algorithms ✅ Describe the methods used to optimize an ML-driven trading strategy. DAY AND SWING TRADING ✅ How Swing trading differs from Day trading in terms of risk-aversion ✅ How your money should be invested and which trade is more profitable ✅ Swing and Day trading proven indicators to learn investment timing ✅ The secret DAY trading strategies leading to a gain of $ 9,000 per month and more than $100,000 per year. OPTIONS TRADING FOR BEGINNERS ✅ Options Trading Strategies that guarantee real results in all market conditions ✅ Top 7 endorsed indicators of a successful investment ✅ The Bull & Bear Game ✅ Learn about the 3 best charts patterns to fluctuations of stock prices Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. Today is the best day to start programming like a pro. For those trading with leverage, looking for a way to take a controlled approach and manage risk, a properly designed trading system is the answer If you really wish to learn MACHINE LEARNING FOR ALGORITHM TRADING and master its language, please click the BUY NOW button.

Machine Learning For Algorithmic Trading

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Author : Stefan Jansen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31

Machine Learning For Algorithmic Trading written by Stefan Jansen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Business & Economics categories.

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data Who this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

Hands On Deep Learning For Finance

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Author : Luigi Troiano
language : en
Publisher:
Release Date : 2020-02-28

Hands On Deep Learning For Finance written by Luigi Troiano and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with Computers categories.

Hands On Financial Trading With Python

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Author : Jiri Pik
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-29

Hands On Financial Trading With Python written by Jiri Pik and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Computers categories.

Build and backtest your algorithmic trading strategies to gain a true advantage in the market Key FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python's data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook Description Creating an effective system to automate your trading can help you achieve two of every trader's key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it's the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you'll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets. What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is for If you're a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You don't have to be a fully-fledged programmer to dive into this book, but knowing how to use Python's core libraries and a solid grasp on statistics will help you get the most out of this book.

Machine Learning For Algorithmic Trading

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Author : Mark Broker
language : en
Publisher: Independently Published
Release Date : 2020-11-22

Machine Learning For Algorithmic Trading written by Mark Broker and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-22 with categories.

Ai Trading Revolution Powerful Machine Learning Strategies With Python Code

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Author : Val June
language : en
Publisher: Independently Published
Release Date : 2025-07-09

Ai Trading Revolution Powerful Machine Learning Strategies With Python Code written by Val June and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Business & Economics categories.

In this revolutionary book, "AI Trading Revolution: Powerful Machine Learning Strategies With Python Code," readers will dive into the world of algorithmic trading and learn how to leverage machine learning techniques to optimize their trading strategies. With the increasing availability of historical market data and advancements in AI technology, traders now have the opportunity to harness the power of machine learning to make more informed and profitable trading decisions. Through step-by-step Python code examples, readers will learn how to build and backtest machine learning models that can predict market trends, identify trading signals, and ultimately improve their trading performance. Whether you are a beginner looking to explore the world of algorithmic trading or an experienced trader seeking to enhance your strategies, this book provides a comprehensive guide to implementing machine learning techniques in the financial markets. By combining theoretical insights with practical examples, "AI Trading Revolution" equips readers with the knowledge and tools needed to navigate the complexities of algorithmic trading and stay ahead in today's fast-paced market environment. From understanding the basics of machine learning and financial markets to implementing advanced trading strategies, this book offers a holistic approach to AI trading that is both accessible and informative. Join the AI trading revolution and discover how machine learning can revolutionize your trading strategies. With "AI Trading Revolution," you will be equipped with the necessary skills to adapt to the evolving landscape of algorithmic trading and unlock new opportunities for success in the financial markets.

Machine Learning For Algorithmic Trading

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Author : Jason Test
language : en
Publisher:
Release Date : 2020-11-20

Machine Learning For Algorithmic Trading written by Jason Test and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-20 with Computers categories.

Master the best methods for PYTHON. Learn how to programming as a pro and get positive ROI in 7 days with data science and machine learning Are you looking for a super-fast computer programming course? Would you like to learn the Python Programming Language in 7 days? Do you want to increase your trading thanks to the artificial intelligence? If so, keep reading: this bundle book is for you! Today, thanks to computer programming and PYTHON we can work with sophisticated machines that can study human behavior and identify underlying human behavioral patterns. Scientists can predict effectively what products and services consumers are interested in. You can also create various quantitative and algorithmic trading strategies using Python. It is getting increasingly challenging for traditional businesses to retain their customers without adopting one or more of the cutting-edge technology explained in this book. MACHINE LEARNING FOR ALGORITHM TRADING will introduce you many selected tips and breaking down the basics of coding applied to finance. You will discover as a beginner the world of data science, machine learning and artificial intelligence with step-by-step guides that will guide you during the code-writing learning process. The following list is just a tiny fraction of what you will learn in this bundle PYTHON FOR BEGINNERS ✅ Differences among programming languages: Vba, SQL, R, Python ✅ 3 reasons why Python is fundamental for Data Science ✅ Introduction to some Python libraries like NumPy, Pandas, Matplotlib, ✅ 3 step system why Python is fundamental for Data Science ✅Describe the steps required to develop and test an ML-driven trading strategy. PYTHON DATA SCIENCE ✅ A Proven Method to Write your First Program in 7 Days ✅ 3 Common Mistakes to Avoid when You Start Coding ✅ Fit Python Data Analysis to your business ✅ 7 Most effective Machine Learning Algorithms ✅ Describe the methods used to optimize an ML-driven trading strategy. OPTIONS TRADING FOR BEGINNERS ✅ Options Trading Strategies that guarantee real results in all market conditions ✅ Top 7 endorsed indicators of a successful investment ✅ The Bull & Bear Game ✅ Learn about the 3 best charts patterns to fluctuations of stock prices DAY AND SWING TRADING ✅ How Swing trading differs from Day trading in terms of risk-aversion ✅ How your money should be invested and which trade is more profitable ✅ Swing and Day trading proven indicators to learn investment timing ✅ The secret DAY trading strategies leading to a gain of $ 9,000 per month and more than $100,000 per year. Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. Today is the best day to start programming like a pro. For those trading with leverage, looking for a way to take a controlled approach and manage risk, a properly designed trading system is the answer If you really wish to learn MACHINE LEARNING FOR ALGORITHMIC TRADING and master its language, please click the BUY NOW button.

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Hands On Ai Trading With Python Quantconnect And Aws

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